Dynamic changes in total organic carbon (TOC) concentration in lakes and reservoirs affect the functions of aquatic ecosystems and are a key component of water quality management, especially in drinking water sources. The Danjiangkou Reservoir is the water source area of the Middle Route Project of the South-to-North Water Diversion in China. Its water quality is of critical importance to the safety of water diversion. TOC concentration and other environmental factors at 19 sampling sites in the Danjiangkou Reservoir were investigated quarterly during 2020–2021 to explore the differences at the spatio-temporal scales. A generalized additive model (GAM) was used to analyze the environmental factors correlated with the observed spatio-temporal variations of TOC concentration. The results showed that the comprehensive trophic level index (TLI) of the Danjiangkou Reservoir was under the state of intermediate nutrition, and the water quality was overall good. In terms of temporal patterns, TOC concentration was higher in both spring and summer and lower in other seasons. Spatially, TOC concentrations were found in descending order from the site of outlet, Han reservoir, entrance of reservoir, and Dan reservoir. The single-factor GAM model showed that TOC correlated with different environmental factors across spatio-temporal scales. Water temperature (WT), permanganate index (CODMn), and ammonia nitrogen (NH4+-N) were significantly correlated with TOC in autumn, but only total nitrogen (TN) and transparency (SD) were significant in winter. Spatially, WT, chemical oxygen demand (COD), NH4+-N, TN, and conductivity (Cond) correlated with TOC in the Dan reservoir, but WT, COD, NH4+-N, total phosphorus (TP), and chlorophyll a (Chl.a) were significant in the Han reservoir. The multi-factor GAM model indicated that the environmental factors correlated with TOC concentration were mainly WT, TN, Cond, CODMn, and TP, among which WT and Cond showed a significant linear relationship with TOC concentration (edf = 1, p < 0.05), while TN, CODMn, and TP had a significant nonlinear relationship with TOC concentration (edf > 1, p < 0.05). Comprehensive trophic level index (TLI) and TOC concentration revealed a highly significant correlation (R2 = 0.414, p < 0.001). Therefore, the GAM model could well explain the environmental factors associated with the spatio-temporal dynamics of TOC concentration, providing a reference for the evaluation of water quality and research on the carbon cycle in similar inland reservoirs.
Near-infrared (NIR) spectroscopy is a rapid nondestructive technique that has been used to characterize chemical and physical properties of a wide range of materials. In this study, transmittance NIR spectra from thin wood wafers cut from increment cores were used to develop calibration models for the estimation of α-cellulose content, average fiber length, fiber coarseness, and lignin content in the laboratory. Eleven-year-old trees from two sites were sampled using 12-mm increment cores. Earlywood and latewood of ring 3 and ring 8 from these samples were analyzed in the laboratory using microanalytical methods for α-cellulose content, average fiber length, fiber coarseness, and lignin content. NIR calibrations and laboratory measurements based on one site were generally reliable, with coefficients of determination (R 2 ) ranging from 0.54 to 0.88 for average fiber length and α-cellulose content, respectively. Predicting ring 8 properties using ring 3 calibration equations showed potential for predicting α-cellulose content and fiber coarseness, with R 2 values of approximately 0.60, indicating the potential for early selection. Predicting the wood properties using the calibration equations from one site to predict another showed moderate success for α-cellulose content (R 2 = 0.64) and fiber coarseness (R 2 = 0.63), but predictions for fiber length were relatively poor (R 2 = 0.43). Prediction of lignin content using transmittance NIR spectroscopy was not as reliable in this study, partially because of low variation in lignin content in these wood samples and large errors in measuring lignin content in the laboratory.
Global dryland areas are vulnerable to climate change and anthropogenic activities, making it essential to understand the primary drivers and quantify their effects on vegetation growth. In this study, we used the Time Series Segmented Residual Trends (TSS-RESTREND) method to attribute changes in vegetation to CO2, land use, climate change, and climate variability in Chinese and American dryland areas. Our analysis showed that both Chinese and American drylands have undergone a greening trend over the past four decades, with Chinese greening likely linked to climatic warming and humidification of Northwest China. Climate change was the dominant factor driving vegetation change in China, accounting for 48.3%, while CO2 fertilization was the dominant factor in American drylands, accounting for 47.9%. However, land use was the primary factor resulting in desertification in both regions. Regional analysis revealed the importance of understanding the drivers of vegetation change and land degradation in Chinese and American drylands to prevent desertification. These findings highlight the need for sustainable management practices that consider the complex interplay of climate change, land use, and vegetation growth in dryland areas.
The green and efficient remediation of soil cadmium (Cd) is an urgent task, and plant-microbial joint remediation has become a research hotspot due to its advantages. High-throughput sequencing and metabolomics have technical advantages in analyzing the microbiological mechanism of plant growth-promoting bacteria in improving phytoremediation of soil heavy metal pollution. In this experiment, a pot trial was conducted to investigate the effects of inoculating the plant growth-promoting bacterium
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